Nuclear Norm Minimization for Blind Subspace Identification (N2BSID)Show others and affiliations
2015 (English)In: 2015 IEEE 54th Annual Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 2127-2132Conference paper, Published paper (Refereed)
Abstract [en]
In many practical applications of system identification, it is not feasible to measure both the inputs applied to the system as well as the output. In such situations, it is desirable to estimate both the inputs and the dynamics of the system simultaneously; this is known as the blind identification problem. In this paper, we provide a novel extension of subspace methods to the blind identification of multiple-input multiple-output linear systems. We assume that our inputs lie in a known subspace, and we are able to formulate the identification problem as rank constrained optimization, which admits a convex relaxation. We show the efficacy of this formulation with a numerical example.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 2127-2132
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-133144DOI: 10.1109/CDC.2015.7402521ISI: 000381554502047ISBN: 978-1-4799-7886-1 (print)ISBN: 978-1-4799-7884-7 (print)ISBN: 978-1-4799-7885-4 (electronic)ISBN: 978-1-4799-7887-8 (print)OAI: oai:DiVA.org:liu-133144DiVA, id: diva2:1055211
Conference
54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, December 15-18, 2015
2016-12-122016-12-092019-01-04Bibliographically approved